Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems

Inaam Ashraf, André Artelt, Barbara Hammer
2/11/2025
cs.NEcs.LGeess.SY

No official code found. Best implementation: inaamashraf/SPIGNNs_for_WDSs (8 stars, last commit 2025-02-19).

Abstract

Water distribution systems (WDSs) are an important part of critical infrastructure becoming increasingly significant in the face of climate change and urban population growth. We propose a robust and scalable surrogate deep learning (DL) model to enable efficient planning, expansion, and rehabilitation of WDSs. Our approach incorporates an improved graph neural network architecture, an adapted physics-informed algorithm, an innovative training scheme, and a physics-preserving data normalization method. Evaluation results on a number of WDSs demonstrate that our model outperforms the current state-of-the-art DL model. Moreover, our method allows us to scale the model to bigger and more realistic WDSs. Furthermore, our approach makes the model more robust to out-of-distribution input features (demands, pipe diameters). Hence, our proposed method constitutes a significant step towards bridging the simulation-to-real gap in the use of artificial intelligence for WDSs.

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Code Implementations(1)

inaamashraf/SPIGNNs_for_WDSsCommunity62%

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems.

84Python, Jupyter NotebookFeb 11, 20251 years ago

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Cite this paper

@article{ashraf2025scalable,
  title  = {Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems},
  author = {Inaam Ashraf and André Artelt and Barbara Hammer},
  year   = {2025},
  eprint = {2502.12164},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.12164v1}
}

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